Search results for: long short-term memory (lstm)

Authors: Milagros Jaén-Vargas, Javier García Martínez, Karla Miriam Reyes Leiva, María Fernanda Trujillo-Guerrero, Francisco Fernandes, Sérgio Barroso Gonçalves, Miguel Tavares Silva, Daniel Simões Lopes, José Javier Serrano Olmedo

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Human Activity Recognition (HAR) is having a growing impact in creating new applications and is responsible for emerging new technologies. Also, the use of wearable sensors is an important key to exploring the human body's behavior when performing activities. Hence, the use of these dispositive is less invasive and the person is more comfortable. In this study, a database that includes three activities is used. The activities were acquired from inertial measurement unit sensors (IMU) and motion capture systems (MOCAP). The main objective is differentiating the performance from four Deep Learning (DL) models: Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and hybrid model Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), when considering acceleration, velocity and position and evaluate if integrating the IMU acceleration to obtain velocity and position represent an increment in performance when it works as input to the DL models. Moreover, compared with the same type of data provided by the MOCAP system. Despite the acceleration data is cleaned when integrating, results show a minimal increase in accuracy for the integrated signals.

Keywords: HAR, IMU, MOCAP, acceleration, velocity, position, feature maps

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Authors: Huawei Bai, Jianguo Yao, Fellow, IEEE

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Atrial fibrillation (AF) is the most common form of heart arrhythmia and is closely associated with mortality and morbidity in heart failure, stroke, and coronary artery disease. The development of single spot optical sensors enables widespread photoplethysmography (PPG) screening, especially for AF, since it represents a more convenient and noninvasive approach. To our knowledge, most existing studies based on public and unbalanced datasets can barely handle the multiple noises sources in the real world and, also, lack interpretability. In this paper, we construct a large- scale PPG dataset using measurements collected from PPG wrist- watch devices worn by volunteers and propose an attention-based two-stream heterogeneous neural network (TSHNN). The first stream is a hybrid neural network consisting of a three-layer one-dimensional convolutional neural network (1D-CNN) and two-layer attention- based bidirectional long short-term memory (Bi-LSTM) network to learn representations from temporally sampled signals. The second stream extracts latent representations from the PPG time-frequency spectrogram using a five-layer CNN. The outputs from both streams are fed into a fusion layer for the outcome. Visualization of the attention weights learned demonstrates the effectiveness of the attention mechanism against noise. The experimental results show that the TSHNN outperforms all the competitive baseline approaches and with 98.09% accuracy, achieves state-of-the-art performance.

Keywords: PPG wearables, atrial fibrillation, feature fusion, attention mechanism, hyber network

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Authors: Rema Vazhappilly, Tapas Das

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Oleic acid is a cis unsaturated fatty acid and is known to be a partially essential fatty acid due to its limited endogenous synthesis during pregnancy and lactation. Previous studies have demonstrated the role of oleic acid in neuronal differentiation and brain phospholipid synthesis. These evidences indicate a major role for oleic acid in learning and memory. Interestingly, oleic acid has been shown to enhance hippocampal long term potentiation (LTP), the physiological correlate of long term synaptic plasticity. However the effect of oleic acid on short term synaptic plasticity has not been investigated. Short term potentiation (STP) is the physiological correlate of short term synaptic plasticity which is the key underlying molecular mechanism of short term memory and neuronal information processing. STP in the hippocampal CA1 region has been known to require the activation of N-methyl-D-aspartate receptors (NMDARs). The NMDAR dependent hippocampal STP as a potential mechanism for short term memory has been a subject of intense interest for the past few years. Therefore in the present study the effect of oleic acid on NMDAR dependent hippocampal STP was determined in mouse hippocampal slices (in vitro) using Multi-electrode array system. STP was induced by weak tetanic Stimulation (one train of 100 Hz stimulations for 0.1s) of the Schaffer collaterals of CA1 region of the hippocampus in slices treated with different concentrations of oleic acid in presence or absence of NMDAR antagonist D-AP5 (30 µM) . Oleic acid at 20 (mean increase in fEPSP amplitude = ~135 % Vs. Control = 100%; P<0.001) and 30 µM (mean increase in fEPSP amplitude = ~ 280% Vs. Control = 100%); P<0.001) significantly enhanced the STP following weak tetanic stimulation. Lower oleic acid concentrations at 10 µM did not modify the hippocampal STP induced by weak tetanic stimulation. The hippocampal STP induced by weak tetanic stimulation was completely blocked by the NMDA receptor antagonist D-AP5 (30µM) in both oleic acid and control treated hippocampal slices. This lead to the conclusion that the hippocampal STP elicited by weak tetanic stimulation and enhanced by oleic acid was NMDAR dependent. Together these findings suggest that oleic acid may enhance the short term memory and neuronal information processing through the modulation of NMDAR dependent hippocampal short-term synaptic plasticity. In conclusion this study suggests the possible role of oleic acid to prevent the short term memory loss and impaired neuronal function throughout development.

Keywords: oleic acid, short-term potentiation, memory, field excitatory post synaptic potentials, NMDA receptor

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Authors: Akiko Kondo

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Although the effects of working memory capacity on second/foreign language speaking skills have been researched in depth, few studies have focused on Japanese English as a foreign language (EFL) learners as compared to other languages (Indo-European languages), and the sample sizes of the relevant Japanese studies have been relatively small. Furthermore, comparing the effects of working memory capacity and motivation which is another kind of frequently researched individual factor on L2 speaking skills would add to the scholarly literature in the field of second language acquisition research. Therefore, the purposes of this study were to investigate whether working memory capacity and motivation have significant relationships with Japanese EFL learners’ speaking skills and to investigate the degree to which working memory capacity and motivation contribute to their English speaking skills. One-hundred and ten Japanese EFL students aged 18 to 26 years participated in this study. All of them are native Japanese speakers and have learned English as s foreign language for 6 to 15. They completed the Versant English speaking test, which has been widely used to measure non-native speakers’ English speaking skills, two types of working memory tests (the L1-based backward digit span test and the L1-based listening span test), and the language learning motivation survey. The researcher designed the working memory tests and the motivation survey. To investigate the relationship between the variables (English speaking skills, working memory capacity, and language learning motivation), a correlation analysis was conducted, which showed that L2 speaking test scores were significantly related to both working memory capacity and language learning motivation, although the correlation coefficients were weak. Furthermore, a multiple regression analysis was performed, with L2 speaking skills as the dependent variable and working memory capacity and language learning motivation as the independent variables. The results showed that working memory capacity and motivation significantly explained the variance in L2 speaking skills and that the L2 motivation had slightly larger effects on the L2 speaking skills than the working memory capacity. Although this study includes several limitations, the results could contribute to the generalization of the effects of individual differences, such as working memory and motivation on L2 learning, in the literature.

Keywords: individual differences, motivation, speaking skills, working memory

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Authors: Q. B. Maria de la Luz Ortega Juárez, Saúl Rojas Hernández, Itzel Berenice Rodríguez Mera, María Maricela Carrasco Yépez, Mara Gutierrez Sánchez

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Naegleria fowleri is a free-living amoeba found mainly in temperate freshwater and is the etiologic agent of primary amebic meningoencephalitis (PAM), a fatal acute disease with a mortality rate greater than 95%. At present, there are no treatments available for MAP, and the development of effective vaccines that generate long-term immunological memory allowing protection against MAP would be of great importance. The objective of this work was to analyze the effector and memory immune response in BALB/c mice immunized with total extract of N. fowleri co-administered with cholera toxin. In this study, BALB/c mice were immunized four times intranasally with ET of N. fowleri adjuvanted with CT with or without booster at three months and were challenged or not with the lethal dose of N. fowleri, determining survival, the humoral, effector and memory response, by ELISA and flow cytometry techniques. The results obtained showed that the survival of mice immunized with booster had 60% protection compared to the group without booster, which obtained 20% protection. Evaluating the humoral response, it was found that both IgG and IgA levels were higher in sera than in nasal washes in both treatments. In the cellular response, the increase in the percentage of positive cells was found for effector T and B lymphocytes in the nasal passages (NP) in the group with boost and nasopharynx-associated lymphoid tissue (NALT) in the group without boost and lymphocytes only. B in both treatments, as well as in memory cells treatment with boost T lymphocytes in PN and NALT and without boost in cervical lymph nodes (CG) with respect to B lymphocytes, in PN, GC and NALT in treatment with boost and NALT in treatment without booster. Therefore, the involvement of the effector immune response and memory play a fundamental role for protection against N. fowleri and for the development of vaccine candidates.

Keywords: immune response, immunological memory, naegleria fowleri, primary amebic meningoencephalitis

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Authors: Ana B. Vivas, Antonia Ypsilanti, Aristea I. Ladas, Angeles F. Estevez

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Mild Cognitive Impairment (MCI) is considered an intermediate stage between normal and pathological aging, as a substantial percentage of people diagnosed with MCI converts later to dementia of the Alzheimer’s type. Memory is of the first cognitive processes to deteriorate in this condition. In the present study we employed the differential outcomes procedure (DOP) to improve visuospatial memory in a group of participants with MCI. The DOP requires the structure of a conditional discriminative learning task in which a correct choice response to a specific stimulus-stimulus association is reinforced with a particular reinforcer or outcome. A group of 10 participants with MCI, and a matched control group had to learn and keep in working memory four target locations out of eight possible locations where a shape could be presented. Results showed that participants with MCI had a statistically significant better terminal accuracy when a unique outcome was paired with a location (76% accuracy) as compared to a non differential outcome condition (64%). This finding suggests that the DOP is useful in improving working memory in MCI patients, which may delay their conversion to dementia.

Keywords: mild cognitive impairment, working memory, differential outcomes, cognitive process

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Authors: Yueying Chen

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A city can be developed through memory, and memory is one of the most important elements for urban contexts. Collective memory is a collection of personal memories that can be preserved with objects, places, and events of heritage, expressing culture through spatial changes. These preserved forms can evoke a sense of community and certain emotions. Collective memory in cities reflects urban spatial alterations and historical developments. It can be preserved and reflected by revitalisation projects. A major current focus in collective memory research is how to identify and preserve memory in an intangible way. The influential elements within the preservation of collective memory mainly include institutions and objects. However, current research lacks the assessment of the collective memory after alterations of urban heritage. The assessment of urban heritage lacks visualization and qualitative methods. The emergence of the application of space syntax can fill in this gap. Hengshan Mansions was a new project in 2015. The original residential area has been replaced with a comprehensive commercial area integrating boutique shopping, upscale restaurants, and creative offices. Hengshan Mansions is located in the largest historic area in Shanghai, and its development is the epitome of the traditional culture in Shanghai. Its alteration is the newest project in this area and presents the new concept of revitalisation of urban heritage. For its physical parts, modern vitality is created, and historical information is preserved at the same time. However, most of the local people are moved away, and its functions are altered a lot. The preservation of its collective memory needs to discuss furtherly. Thus, the article builds a framework to assess the collective memory of urban heritage, including spatial configuration, spatial interaction, and cultural cognition. Then, it selects Hengshan Mansions in Shanghai as a case to analyse the assessed framework. Space syntax can be applied to visualize the assessment. Based on the analysis, the article will explore the influential reasons for the collective memory after alterations and proposes relevant advice for the preservation of the collective memory of urban heritage.

Keywords: collective memory, alternation of urban heritage, space syntax, Hengshan Mansions

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Authors: Asher John Mohan, Krishna K. L.

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Objectives: (1) To assess the extent of memory impairment induced by Phenytoin (PHT) at normal and reduced dose on temporal lobe epileptic mice. (2) To evaluate the protective effect of Levetiracetam (LEV) on aggravation of memory impairment in temporal lobe epileptic mice by PHT. Materials and Methods: Albino mice of either sex (n=36) were used for the study for a period of 64 days. Convulsions were induced by intraperitoneal administration of pilocarpine 280 mg/kg on every 6th day. Radial arm maze (RAM) was employed to evaluate the memory impairment activity on every 7th day. The anticonvulsant and memory impairment activity were assessed in PHT normal and reduced doses both alone and in combination with LEV. RAM error scores and convulsive scores were the parameters considered for this study. Brain acetylcholine esterase and glutamate were determined along with histopathological studies of frontal cortex. Results: Administration of PHT for 64 days on mice has shown aggravation of memory impairment activity on temporal lobe epileptic mice. Although the reduction in PHT dose was found to decrease the degree of memory impairment the same decreased the anticonvulsant potency. The combination with LEV not only brought about the correction of impaired memory but also replaced the loss of potency due to the reduction of the dose of the antiepileptic drug employed. These findings were confirmed with enzyme and neurotransmitter levels in addition to histopathological studies. Conclusion: This study thus builds a foundation in combining a nootropic anticonvulsant with an antiepileptic drug to curb the adverse effect of memory impairment associated with temporal lobe epilepsy. However further extensive research is a must for the practical incorporation of this approach into disease therapy.

Keywords: anti-epileptic drug, Phenytoin, memory impairment, Pilocarpine

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Authors: Kamyar Moradi

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Background: There is mixed evidence regarding the association between recreational cannabinoid use and memory performance. One of the major reasons for the present controversy is different cannabinoid use-related covariates that influence the cognitive status of an individual. Adjustment of these confounding variables provides accurate insight into the real effects of cannabinoid use on memory status. In this study, we sought to investigate the association between recent recreational cannabinoid use and memory performance while correcting the model for other possible covariates such as demographic characteristics and duration, and amount of cannabinoid use. Methods: Cannabinoid users were assigned to two groups based on the results of THC urine drug screen test (THC+ group: n = 110, THC- group: n = 410). THC urine drug screen test has a high sensitivity and specificity in detecting cannabinoid use in the last 3-4 weeks. The memory domain of NIH Toolbox battery and brain MRI volumetric measures were compared between the groups while adjusting for confounding variables. Results: After Benjamini-Hochberg p-value correction, the performance in all of the measured memory outcomes, including vocabulary comprehension, episodic memory, executive function/cognitive flexibility, processing speed, reading skill, working memory, and fluid cognition, were significantly weaker in THC+ group (p values less than 0.05). Also, volume of gray matter, left supramarginal, right precuneus, right inferior/middle temporal, right hippocampus, left entorhinal, and right pars orbitalis regions were significantly smaller in THC+ group. Conclusions: this study provides evidence regarding the acute effect of recreational cannabis use on memory performance. Further studies are warranted to confirm the results.

Keywords: brain MRI, cannabis, memory, recreational use, THC urine test

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Authors: Francesco Scarpati

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Because of their long history, ranging from ancient times to the present day, European cities feature many historical layers, whose single identities are represented by traces surviving in the urban design. However, urban transformations, in particular, the ones that have been produced by the property speculation phenomena of the 20th century, often compromised the readability of these traces, resulting in a loss of the historical identities of the single layers. The purpose of this research is, therefore, a reflection on the theme of the reenactment of the historical memory in the stratified European contexts and on how contemporary architecture can help to reveal past signs of the cities. The research work starts from an analysis of a series of emblematic examples that have already provided an original solution to the described problem, going from the architectural detail scale to the urban and landscape scale. The results of these analyses are then applied to the case study of the city of Naples, as an emblematic example of a stratified city, with an ancient Greek origin; a city where it is possible to read most of the traces of its transformations. Particular consideration is given to the trace of the medieval walls of the city, which a long time ago clearly divided the city itself from the outer fields, and that is no longer readable at the current time. Finally, solutions and methods of intervention are proposed to ensure that the trace of the walls, read as a boundary, can be revealed through the contemporary project.

Keywords: contemporary project, historic memory, historic urban contexts, medieval walls, naples, stratified cities, urban traces

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Authors: Yolina A. Petrova, Georgi I. Petkov

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The paper presents a symbolic model for category learning and categorization (called RoleMap). Unlike the other models which implement learning in a separate working mode, role-governed category learning and categorization emerge in RoleMap while it does its usual reasoning. The model is based on several basic mechanisms known as reflecting the sub-processes of analogy-making. It steps on the assumption that in their everyday life people constantly compare what they experience and what they know. Various commonalities between the incoming information (current experience) and the stored one (long-term memory) emerge from those comparisons. Some of those commonalities are considered to be highly important, and they are transformed into concepts for further use. This process denotes the category learning. When there is missing knowledge in the incoming information (i.e. the perceived object is still not recognized), the model makes anticipations about what is missing, based on the similar episodes from its long-term memory. Various such anticipations may emerge for different reasons. However, with time only one of them wins and is transformed into a category member. This process denotes the act of categorization.

Keywords: analogy-making, categorization, category learning, cognitive modeling, role-governed categories

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Authors: Kirsten A. Bartels

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'Never again' and 'Lest we forget' have long been messages associated with the events of the Shoah. Yet as we attempt to learn from the past, we must find new ways to engage with its memories. The preservation of the culture and the value of tradition are critical factors in Jane Yolen's works of Holocaust fiction, The Devil's Arithmetic and Mapping the Bones, emphasized through the importance of remembering. That word, in its multitude of forms (remember, remembering, memories), occurs no less than ten times in the first four pages and over one hundred times in the one hundred and sixty-four-page narrative The Devil’s Arithmetic. While Yolen takes a different approach to showcasing the importance of memory in Mapping the Bones, it is of equal import in this work and arguably to the future of Holocaust knowing. The idea of remembering, the desire to remember, and the ability to remember, are explored in three divergent ways in The Devil’s Arithmetic. First, in the importance to remember a past which is not her own – to understand history or acquired memories. Second, in the protagonist's actual or initial memories, those of her life in modern-day New York. Third, in a reverse mode of forgetting and trying to reacquire that which has been lost -- as Hannah is processed in the camp and she forgets everything, all worlds prior to the camp are lost to her. As numbers replace names, Yolen stresses the importance of self-identity or owned memories. In addition, the importance of relaying memory, the transitions of memory from perspective, and the ideas of reflective telling are explored in Mapping the Bones -- through the telling of the story through the lens of one of the twins as the events are unfolding; and then the through the reflective telling from the lens of the other twin. Parallel to the exploration of the intersemiosis of memory is the discussion of literary shadows (foreshadowing, backshadowing, and side-shadowing) and their impact on the reader's experience with Yolen's narrative. For in this type of exploration, one cannot look at the events described in Yolen's work and not also contemplate the figurative shadows cast.

Keywords: holocaust literature, memory, narrative, Yolen

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Authors: S. Rozhkova, O. Rozhkova, A. Harlova, V. Lasukov

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We have conducted the optimal synthesis of root-mean-squared objective filter to estimate the state vector in the case if within the observation channel with memory the anomalous noises with unknown mathematical expectation are complement in the function of the regular noises. The synthesis has been carried out for linear stochastic systems of continuous-time.

Keywords: mathematical expectation, filtration, anomalous noise, memory

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Authors: Muhammad Zubair Khan, Yugyung Lee

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Deep learning has recently achieved enormous response in semantic image segmentation. The previously developed U-Net inspired architectures operate with continuous stride and pooling operations, leading to spatial data loss. Also, the methods lack establishing long-term pixels connection to preserve context knowledge and reduce spatial loss in prediction. This article developed encoder-decoder architecture with bi-directional LSTM embedded in long skip-connections and densely connected convolution blocks. The network non-linearly combines the feature maps across encoder-decoder paths for finding dependency and correlation between image pixels. Additionally, the densely connected convolutional blocks are kept in the final encoding layer to reuse features and prevent redundant data sharing. The method applied batch-normalization for reducing internal covariate shift in data distributions. The empirical evidence shows a promising response to our method compared with other semantic segmentation techniques.

Keywords: deep learning, semantic segmentation, image analysis, pixels connection, convolution neural network

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Authors: Amjad Nusayr

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Software security is a general term used to any type of software architecture or model in which security aspects are incorporated in this architecture. These aspects are not part of the main logic of the underlying program. Software security can be achieved using a combination of approaches, including but not limited to secure software designs, third part component validation, and secure coding practices. Memory safety is one feature in software security where we ensure that any object in memory has a valid pointer or a reference with a valid type. Aspect-Oriented Programming (AOP) is a paradigm that is concerned with capturing the cross-cutting concerns in code development. AOP is generally used for common cross-cutting concerns like logging and DB transaction managing. In this paper, we introduce the concepts that enable AOP to be used for the purpose of memory and type safety. We also present ideas for extending AOP in software security practices.

Keywords: aspect oriented programming, programming languages, software security, memory and type safety

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Authors: Supria Sarkar, Vasit Sagan, Sourav Bhadra, Meghnath Pokharel, Felix B.Fritschi

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Soybean and their derivatives are very important agricultural commodities around the world because of their wide applicability in human food, animal feed, biofuel, and industries. However, the significance of soybean production depends on the quality of the soybean seeds rather than the yield alone. Seed composition is widely dependent on plant physiological properties, aerobic and anaerobic environmental conditions, nutrient content, and plant phenological characteristics, which can be captured by high temporal resolution remote sensing datasets. Planet scope (PS) satellite images have high potential in sequential information of crop growth due to their frequent revisit throughout the world. In this study, we estimate soybean seed composition while the plants are in the field by utilizing PlanetScope (PS) satellite images and different machine learning algorithms. Several experimental fields were established with varying genotypes and different seed compositions were measured from the samples as ground truth data. The PS images were processed to extract 462 hand-crafted vegetative and textural features. Four machine learning algorithms, i.e., partial least squares (PLSR), random forest (RFR), gradient boosting machine (GBM), support vector machine (SVM), and two recurrent neural network architectures, i.e., long short-term memory (LSTM) and gated recurrent unit (GRU) were used in this study to predict oil, protein, sucrose, ash, starch, and fiber of soybean seed samples. The GRU and LSTM architectures had two separate branches, one for vegetative features and the other for textures features, which were later concatenated together to predict seed composition. The results show that sucrose, ash, protein, and oil yielded comparable prediction results. Machine learning algorithms that best predicted the six seed composition traits differed. GRU worked well for oil (R-Squared: of 0.53) and protein (R-Squared: 0.36), whereas SVR and PLSR showed the best result for sucrose (R-Squared: 0.74) and ash (R-Squared: 0.60), respectively. Although, the RFR and GBM provided comparable performance, the models tended to extremely overfit. Among the features, vegetative features were found as the most important variables compared to texture features. It is suggested to utilize many vegetation indices for machine learning training and select the best ones by using feature selection methods. Overall, the study reveals the feasibility and efficiency of PS images and machine learning for plot-level seed composition estimation. However, special care should be given while designing the plot size in the experiments to avoid mixed pixel issues.

Keywords: agriculture, computer vision, data science, geospatial technology

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Authors: Samuel Kaspi, Sitalakshmi Venkatraman

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In-memory database systems are becoming popular due to the availability and affordability of sufficiently large RAM and processors in modern high-end servers with the capacity to manage large in-memory database transactions. While fast and reliable in-memory systems are still being developed to overcome cache misses, CPU/IO bottlenecks and distributed transaction costs, disk-based data stores still serve as the primary persistence. In addition, with the recent growth in multi-tenancy cloud applications and associated security concerns, many organisations consider the trade-offs and continue to require fast and reliable transaction processing of disk-based database systems as an available choice. For these organizations, the only way of increasing throughput is by improving the performance of disk-based concurrency control. This warrants a hybrid database system with the ability to selectively apply an enhanced disk-based data management within the context of in-memory systems that would help improve overall throughput. The general view is that in-memory systems substantially outperform disk-based systems. We question this assumption and examine how a modified variation of access invariance that we call enhanced memory access, (EMA) can be used to allow very high levels of concurrency in the pre-fetching of data in disk-based systems. We demonstrate how this prefetching in disk-based systems can yield close to in-memory performance, which paves the way for improved hybrid database systems. This paper proposes a novel EMA technique and presents a comparative study between disk-based EMA systems and in-memory systems running on hardware configurations of equivalent power in terms of the number of processors and their speeds. The results of the experiments conducted clearly substantiate that when used in conjunction with all concurrency control mechanisms, EMA can increase the throughput of disk-based systems to levels quite close to those achieved by in-memory system. The promising results of this work show that enhanced disk-based systems facilitate in improving hybrid data management within the broader context of in-memory systems.

Keywords: in-memory database, disk-based system, hybrid database, concurrency control

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Authors: Gagnesh Kumar, Nitin Gupta

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Ternary Content Addressable Memory cells are mainly popular in network routers for packet forwarding and packet classification, but they are also useful in a variety of other applications that require high-speed table look-up. The main TCAM-design challenge is to decrease the power consumption associated with the large amount of parallel active circuitry, without compromising with speed or memory density. Furthermore, when the channel length decreases, leakage power becomes more significant, and it can even dominate dynamic power at lower technologies. In this paper, we propose a TCAM-design technique, called Virtual Power Supply technique that reduces the leakage by a substantial amount.

Keywords: match line (ML), search line (SL), ternary content addressable memory (TCAM), Leakage power (LP)

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Authors: Douglas Rossi Ramos

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The current digital conjuncture, called by some authors as 'Internet of Things' (IoT), 'Web 2.0' or even 'Web 3.0', consists of a network that encompasses any communication of objects and entities, such as data, information, technologies, and people. At this juncture, especially characterized by an "object socialization," communication can no longer be represented as a simple informational flow of messages from a sender, crossing a channel or medium, reaching a receiver. The idea of communication must, therefore, be thought of more broadly in which it is possible to analyze the process communicative from interactions between humans and nonhumans. To think about this complexity, a communicative process that encompasses both humans and other beings or entities communicating (objects and things), it is necessary to constitute a new epistemology of communication to rethink concepts and notions commonly attributed to humans such as 'memory.' This research aims to contribute to this epistemological constitution from the discussion about the notion of memory according to the complex ontology of Henri Bergson. Among the results (the notion of memory in Bergson presents itself as a conceptual tool for the analysis of posthumanism and the anthropomorphic conjuncture of the new advent of digital), there was the need to think about an ontological memory, analyzed as a being itself (being itself of memory), as a strategy for understanding the forms of interaction and communication that constitute the new digital conjuncture, in which communicating beings or entities tend to interact with each other. Rethinking the idea of communication beyond the dimension of transmission in informative sequences paves the way for an ecological perspective of the digital dwelling condition.

Keywords: communication, digital, Henri Bergson, memory

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Authors: Derssie Mebratu

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As a large variety of micro services, web services, social graphic applications, and media applications are continuously developed, it is substantially vital to design and build a reliable, efficient, and faster memory tiering system. Despite limited design, implementation, and deployment in the last few years, several techniques are currently developed to improve a memory tiering system in a cloud. Some of these techniques are to develop an optimal scanning frequency; improve and track pages movement; identify pages that recently accessed; store pages across each tiering, and then identify pages as a hot, warm, and cold so that hot pages can store in the first tiering Dynamic Random Access Memory (DRAM) and warm pages store in the second tiering Compute Express Link(CXL) and cold pages store in the third tiering Non-Volatile Memory (NVM). Apart from the current proposal and implementation, we also develop a new technique based on a machine learning algorithm in that the throughput produced 25% improved performance compared to the performance produced by the baseline as well as the latency produced 95% improved performance compared to the performance produced by the baseline.

Keywords: machine learning, bayesian optimization, memory tiering, CXL, DRAM

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Authors: Jinny Hong, Jeanne C. Watson

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Three types of autobiographical memory recall -specific, episodic, and generic- were examined in relation to the quality of descriptions and in-session levels of emotional arousal. Correlational analyses and general estimating equation were conducted to test the relationships between 1) quality of descriptions and type of memory, 2) type of memory and emotional arousal, and 3) quality of descriptions and emotional arousal. The data was transcripts drawn from an archival randomized-control study comparing cognitive-behavioral therapy and emotion-focused therapy in a 16-week treatment for depression. Autobiographical memory recall segments were identified and sorted into three categories: specific, episodic, and generic. Quality of descriptions of these segments was then operationalized and measured using the Referential Activity Scale, and each memory segment was rated on four dimensions: concreteness, specificity, clarity, and overall imagery. Clients’ level of emotional arousal for each recall was measured using the Client’s Expression Emotion Scale. Contrary to the predictions, generic memories are associated with higher emotional arousal ratings and descriptive language ratings compared to specific memories. However, a positive relationship emerged between the quality of descriptions and expressed emotional arousal, indicating that the quality of descriptions in which memories are described in sessions is more important than the type of memory recalled in predicting clients’ level of emotional arousal. The results from this study provide a clearer understanding of the role of memory recall types and use of language in activating emotional arousal in psychotherapy sessions in a depressed sample.

Keywords: autobiographical memory recall, emotional arousal, psychotherapy for depression, quality of descriptions, referential activity

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Authors: Vishwanath Pethri Kamath, Jayantha Gowda Sarapanahalli, Vishal Mishra, Siddhesh Balwant Bandgar

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Recognition of emotional information is essential in any form of communication. Growing HCI (Human-Computer Interaction) in recent times indicates the importance of understanding of emotions expressed and becomes crucial for improving the system or the interaction itself. In this research work, textual data for emotion recognition is used. The text being the least expressive amongst the multimodal resources poses various challenges such as contextual information and also sequential nature of the language construction. In this research work, the proposal is made for a neural architecture to resolve not less than 8 emotions from textual data sources derived from multiple datasets using google pre-trained word2vec word embeddings and a Multi-head attention-based bidirectional LSTM model with a one-vs-all Multi-Level Classification. The emotions targeted in this research are Anger, Disgust, Fear, Guilt, Joy, Sadness, Shame, and Surprise. Textual data from multiple datasets were used for this research work such as ISEAR, Go Emotions, Affect datasets for creating the emotions’ dataset. Data samples overlap or conflicts were considered with careful preprocessing. Our results show a significant improvement with the modeling architecture and as good as 10 points improvement in recognizing some emotions.

Keywords: text emotion recognition, bidirectional LSTM, multi-head attention, multi-level classification, google word2vec word embeddings

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Authors: Jayati Bhattacharjee

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Teaching is a very dynamic profession. Teaching Science is as much challenging as Learning the subject if not more. For instance teaching of Chemistry. From the introductory concepts of subatomic particles to atoms of elements and their symbols and further presenting the chemical equation and so forth is a challenge on both side of the equation Teaching Learning. This paper combines the Neuroscience of Learning and memory with the knowledge of Learning style (VAK) and presents an effective tool for the teacher to authenticate Learning. The model of ‘Working Memory’, the Visio-spatial sketchpad, the central executive and the phonological loop that transforms short-term memory to long term memory actually supports the psychological theory of Learning style i.e. Visual –Auditory-Kinesthetic. A closer examination of David Kolbe’s learning model suggests that learning requires abilities that are polar opposites, and that the learner must continually choose which set of learning abilities he or she will use in a specific learning situation. In grasping experience some of us perceive new information through experiencing the concrete, tangible, felt qualities of the world, relying on our senses and immersing ourselves in concrete reality. Others tend to perceive, grasp, or take hold of new information through symbolic representation or abstract conceptualization – thinking about, analyzing, or systematically planning, rather than using sensation as a guide. Similarly, in transforming or processing experience some of us tend to carefully watch others who are involved in the experience and reflect on what happens, while others choose to jump right in and start doing things. The watchers favor reflective observation, while the doers favor active experimentation. Any lesson plan based on the model of Prescriptive design: C+O=M (C: Instructional condition; O: Instructional Outcome; M: Instructional method). The desired outcome and conditions are independent variables whereas the instructional method is dependent hence can be planned and suited to maximize the learning outcome. The assessment for learning rather than of learning can encourage, build confidence and hope amongst the learners and go a long way to replace the anxiety and hopelessness that a student experiences while learning Science with a human touch in it. Application of this model has been tried in teaching chemistry to high school students as well as in workshops with teachers. The response received has proven the desirable results.

Keywords: working memory model, learning style, prescriptive design, assessment for learning

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Authors: Mary Dorothy Roxas, Jeian Mae Dungca, Reginald Agor, Beatriz Figueroa, Lennon Andre Patricio, Honey Joy Cabahug

Abstract:

Parentification occurs when children are expected to provide instrumental or emotional caregiving within the family. It was found that parentification has the latter effect on adolescents’ cognitive and emotional vulnerability. Attachment theory helps clarify the process of parentification as it involves the relationship between the child and the parent. Carandang theory of “taga-salo” helps explain parentification in the Philippines setting. The present study examined the potential risk of parentification on adolescent’s memory retention by hypothesizing that there is a correlation between the two. The research was conducted with 249 female adolescents ages 12-24, residing in Valenzuela City. Results indicated that there is a significant inverse correlation between parentification and memory retention.

Keywords: memory retention, neurocognitive, parentification, stress

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Authors: Motonobu Hattori

Abstract:

In neural networks, when new patters are learned by a network, they radically interfere with previously stored patterns. This drawback is called catastrophic forgetting. We have already proposed a biologically inspired dual-network memory model which can much reduce this forgetting for static patterns. In this model, information is first stored in the hippocampal network, and thereafter, it is transferred to the neocortical network using pseudo patterns. Because, temporal sequence learning is more important than static pattern learning in the real world, in this study, we improve our conventional dual-network memory model so that it can deal with temporal sequences without catastrophic forgetting. The computer simulation results show the effectiveness of the proposed dual-network memory model.

Keywords: catastrophic forgetting, dual-network, temporal sequences, hippocampal

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Authors: Joseph George, Anne Kotteswara Roa

Abstract:

Skin disease is one of the most common and popular kinds of health issues faced by people nowadays. Skin cancer (SC) is one among them, and its detection relies on the skin biopsy outputs and the expertise of the doctors, but it consumes more time and some inaccurate results. At the early stage, skin cancer detection is a challenging task, and it easily spreads to the whole body and leads to an increase in the mortality rate. Skin cancer is curable when it is detected at an early stage. In order to classify correct and accurate skin cancer, the critical task is skin cancer identification and classification, and it is more based on the cancer disease features such as shape, size, color, symmetry and etc. More similar characteristics are present in many skin diseases; hence it makes it a challenging issue to select important features from a skin cancer dataset images. Hence, the skin cancer diagnostic accuracy is improved by requiring an automated skin cancer detection and classification framework; thereby, the human expert’s scarcity is handled. Recently, the deep learning techniques like Convolutional neural network (CNN), Deep belief neural network (DBN), Artificial neural network (ANN), Recurrent neural network (RNN), and Long and short term memory (LSTM) have been widely used for the identification and classification of skin cancers. This survey reviews different DL techniques for skin cancer identification and classification. The performance metrics such as precision, recall, accuracy, sensitivity, specificity, and F-measures are used to evaluate the effectiveness of SC identification using DL techniques. By using these DL techniques, the classification accuracy increases along with the mitigation of computational complexities and time consumption.

Keywords: skin cancer, deep learning, performance measures, accuracy, datasets

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Authors: Hope Amba Neji

Abstract:

The study adopted a quasi-experimental design. Two schools were used for the experimental study, while one school was used for the control. The experimental groups were subjected to treatment for four weeks with chemistry museum artifacts and a visit as made to the museum so that learners would have real-life learning experiences with museum resources, while the control group was taught with the conventional method. The instrument for the study was a 20-item Chemistry Memory Test (CMT) and a 10-item Chemistry Interest Questionnaire (CIQ). The reliability was ascertained using (KR-20) and alpha reliability coefficient, which yielded a reliability coefficient of .83 and .81, respectively. Data obtained was analyzed using Analysis of Covariance (ANCOVA) and Analysis of variance (ANOVA) at 0.05 level of significance. Findings revealed that museum artifacts have a significant effect on students’ memory enhancement and interest in chemistry. It was recommended chemistry learning should be enhanced, motivating and real with museum artifacts, which significantly aid memory enhancement and interest in chemistry.

Keywords: museum artifacts, memory, chemistry, atitude

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Authors: Chhavi Saxena

Abstract:

FinFETs can be a replacement for bulk-CMOS transistors in many different designs. Its low leakage/standby power property makes FinFETs a desirable option for memory sub-systems. Memory modules are widely used in most digital and computer systems. Leakage power is very important in memory cells since most memory applications access only one or very few memory rows at a given time. As technology scales down, the importance of leakage current and power analysis for memory design is increasing. In this paper, we discover an option for low power interconnect synthesis at the 32nm node and beyond, using Fin-type Field-Effect Transistors (FinFETs) which are a promising substitute for bulk CMOS at the considered gate lengths. We consider a mechanism for improving FinFETs efficiency, called variable supply voltage schemes. In this paper, we’ve illustrated the design and implementation of FinFET based 4x4 SRAM cell array by means of one bit 7T SRAM. FinFET based 7T SRAM has been designed and analysis have been carried out for leakage current, dynamic power and delay. For the validation of our design approach, the output of FinFET SRAM array have been compared with standard CMOS SRAM and significant improvements are obtained in proposed model.

Keywords: FinFET, 7T SRAM cell, leakage current, delay

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Authors: Derejaw Lake Melie, Alemu Kumlachew Tegegne

Abstract:

The increasing prevalence of social media in Ethiopia has exacerbated societal challenges by fostering the proliferation of negative emotional posts and comments. Illicit use of social media has further exacerbated divisions among the population. Addressing these issues through manual identification and aggregation of emotions from millions of users for swift decision-making poses significant challenges, particularly given the rapid growth of Amharic language usage on social platforms. Consequently, there is a critical need to develop an intelligent system capable of automatically detecting and categorizing negative emotional content into social, religious, and political categories while also filtering out toxic online content. This paper aims to leverage sentiment analysis techniques to achieve automatic detection and filtering of negative emotional content from Amharic social media texts, employing a comparative study of deep learning algorithms. The study utilized a dataset comprising 29,962 comments collected from social media platforms using comment exporter software. Data pre-processing techniques were applied to enhance data quality, followed by the implementation of deep learning methods for training, testing, and evaluation. The results showed that CNN, GRU, LSTM, and Bi-LSTM classification models achieved accuracies of 83%, 50%, 84%, and 86%, respectively. Among these models, Bi-LSTM demonstrated the highest accuracy of 86% in the experiment.

Keywords: negative emotion, emotion detection, social media filtering sentiment analysis, deep learning.

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Authors: Haidy S. Ghali, Engy Serag, A. Samer Ezeldin

Abstract:

Copper is an essential raw material used in the construction industry. During the year 2021 and the first half of 2022, the global market suffered from a significant fluctuation in copper raw material prices due to the aftermath of both the COVID-19 pandemic and the Russia-Ukraine war, which exposed its consumers to an unexpected financial risk. Thereto, this paper aims to develop two ANN-LSTM price prediction models, using Python, that can forecast the average monthly copper prices traded in the London Metal Exchange; the first model is a multivariate model that forecasts the copper price of the next 1-month and the second is a univariate model that predicts the copper prices of the upcoming three months. Historical data of average monthly London Metal Exchange copper prices are collected from January 2009 till July 2022, and potential external factors are identified and employed in the multivariate model. These factors lie under three main categories: energy prices and economic indicators of the three major exporting countries of copper, depending on the data availability. Before developing the LSTM models, the collected external parameters are analyzed with respect to the copper prices using correlation and multicollinearity tests in R software; then, the parameters are further screened to select the parameters that influence the copper prices. Then, the two LSTM models are developed, and the dataset is divided into training, validation, and testing sets. The results show that the performance of the 3-Month prediction model is better than the 1-Month prediction model, but still, both models can act as predicting tools for diverse economic situations.

Keywords: copper prices, prediction model, neural network, time series forecasting

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